Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells17503
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 3 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
blade_angle has 2428 (4.6%) missing valuesMissing
Rear bearing temperature (°C) has 2428 (4.6%) missing valuesMissing
Nacelle ambient temperature (°C) has 2428 (4.6%) missing valuesMissing
Front bearing temperature (°C) has 2428 (4.6%) missing valuesMissing
Tower Acceleration X (mm/ss) has 2429 (4.6%) missing valuesMissing
Tower Acceleration y (mm/ss) has 2429 (4.6%) missing valuesMissing
Metal particle count counter has 2429 (4.6%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 25123 (47.8%) zerosZeros
Rotor speed (RPM) has 1142 (2.2%) zerosZeros

Reproduction

Analysis started2023-07-08 10:25:26.617349
Analysis finished2023-07-08 10:25:43.760001
Duration17.14 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T15:55:43.812060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:43.905499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52015
Distinct (%)99.1%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean604.46622
Minimum-17.846463
Maximum2081.0289
Zeros1
Zeros (%)< 0.1%
Negative5059
Negative (%)9.6%
Memory size410.8 KiB
2023-07-08T15:55:44.011851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-17.846463
5-th percentile-0.94
Q1123.222
median392.91901
Q3910.9049
95-th percentile1970.0819
Maximum2081.0289
Range2098.8754
Interquartile range (IQR)787.6829

Descriptive statistics

Standard deviation603.4018
Coefficient of variation (CV)0.99823908
Kurtosis0.0030654734
Mean604.46622
Median Absolute Deviation (MAD)327.00511
Skewness1.0534187
Sum31719969
Variance364093.73
MonotonicityNot monotonic
2023-07-08T15:55:44.109859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5899999738 14
 
< 0.1%
-0.5699999928 10
 
< 0.1%
-0.5799999833 10
 
< 0.1%
-0.5500000119 9
 
< 0.1%
-0.5600000024 8
 
< 0.1%
-0.8100000024 8
 
< 0.1%
-0.8000000119 8
 
< 0.1%
-0.9499999881 7
 
< 0.1%
-0.9700000286 7
 
< 0.1%
-0.9399999976 7
 
< 0.1%
Other values (52005) 52388
99.7%
(Missing) 84
 
0.2%
ValueCountFrequency (%)
-17.84646273 1
< 0.1%
-16.51569996 1
< 0.1%
-16.4191236 1
< 0.1%
-16.00296015 1
< 0.1%
-15.80988641 1
< 0.1%
-15.70302417 1
< 0.1%
-15.63368263 1
< 0.1%
-15.24306107 1
< 0.1%
-14.94799824 1
< 0.1%
-14.6467135 1
< 0.1%
ValueCountFrequency (%)
2081.028912 1
< 0.1%
2076.404791 1
< 0.1%
2074.303186 1
< 0.1%
2074.137793 1
< 0.1%
2073.278802 1
< 0.1%
2073.169809 1
< 0.1%
2072.596924 1
< 0.1%
2072.5612 1
< 0.1%
2072.248547 1
< 0.1%
2071.407496 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52382
Distinct (%)99.8%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean203.89267
Minimum0.0073102628
Maximum359.99609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:44.208479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0073102628
5-th percentile35.294631
Q1146.15067
median220.4355
Q3267.143
95-th percentile335.75908
Maximum359.99609
Range359.98878
Interquartile range (IQR)120.99232

Descriptive statistics

Standard deviation89.592227
Coefficient of variation (CV)0.43940878
Kurtosis-0.54677866
Mean203.89267
Median Absolute Deviation (MAD)56.216694
Skewness-0.53022213
Sum10699471
Variance8026.7671
MonotonicityNot monotonic
2023-07-08T15:55:44.309273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350.4200134 3
 
< 0.1%
131.3800049 3
 
< 0.1%
8.970000267 3
 
< 0.1%
51.18000031 3
 
< 0.1%
88.70999908 2
 
< 0.1%
357.5100098 2
 
< 0.1%
317.9599915 2
 
< 0.1%
358.5599976 2
 
< 0.1%
301.6300049 2
 
< 0.1%
126.5599976 2
 
< 0.1%
Other values (52372) 52452
99.8%
(Missing) 84
 
0.2%
ValueCountFrequency (%)
0.007310262753 1
< 0.1%
0.007387221988 1
< 0.1%
0.02536706087 1
< 0.1%
0.04510998095 1
< 0.1%
0.05189457053 1
< 0.1%
0.06546987363 1
< 0.1%
0.07668872178 1
< 0.1%
0.08181672933 1
< 0.1%
0.08806042902 1
< 0.1%
0.105101239 1
< 0.1%
ValueCountFrequency (%)
359.9960879 1
< 0.1%
359.980011 1
< 0.1%
359.9647849 1
< 0.1%
359.9318306 1
< 0.1%
359.9233093 1
< 0.1%
359.8937779 1
< 0.1%
359.8800049 1
< 0.1%
359.8599854 2
< 0.1%
359.8400329 1
< 0.1%
359.7946663 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct12449
Distinct (%)23.7%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean203.69071
Minimum0.011559122
Maximum359.97689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:44.416276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.011559122
5-th percentile33.487984
Q1145.43851
median220.39652
Q3267.26871
95-th percentile335.35431
Maximum359.97689
Range359.96533
Interquartile range (IQR)121.8302

Descriptive statistics

Standard deviation89.805222
Coefficient of variation (CV)0.44089012
Kurtosis-0.54607359
Mean203.69071
Median Absolute Deviation (MAD)56.069956
Skewness-0.5409034
Sum10688874
Variance8064.9778
MonotonicityNot monotonic
2023-07-08T15:55:44.518785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294.7078247 224
 
0.4%
206.9026489 213
 
0.4%
246.414978 205
 
0.4%
212.3904724 196
 
0.4%
211.2929077 188
 
0.4%
299.0975647 186
 
0.4%
244.2193298 182
 
0.3%
209.0972595 182
 
0.3%
48.85383606 178
 
0.3%
250.804718 176
 
0.3%
Other values (12439) 50546
96.2%
ValueCountFrequency (%)
0.01155912249 1
< 0.1%
0.02639976345 1
< 0.1%
0.04083698878 1
< 0.1%
0.06184441699 1
< 0.1%
0.1219860364 1
< 0.1%
0.1560298659 1
< 0.1%
0.2641461632 1
< 0.1%
0.2875801706 1
< 0.1%
0.317681963 1
< 0.1%
0.3361437544 1
< 0.1%
ValueCountFrequency (%)
359.9768922 1
< 0.1%
359.9462585 1
< 0.1%
359.9440009 1
< 0.1%
359.937279 1
< 0.1%
359.9245099 1
< 0.1%
359.8916623 1
< 0.1%
359.8795312 1
< 0.1%
359.8783367 1
< 0.1%
359.8651214 1
< 0.1%
359.8354856 1
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18178
Distinct (%)36.3%
Missing2428
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean4.9624844
Minimum0
Maximum96.358027
Zeros25123
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:44.629280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.91410651
95-th percentile44.993334
Maximum96.358027
Range96.358027
Interquartile range (IQR)0.91410651

Descriptive statistics

Standard deviation14.143172
Coefficient of variation (CV)2.8500185
Kurtosis13.448621
Mean4.9624844
Median Absolute Deviation (MAD)0
Skewness3.5339009
Sum248779.27
Variance200.02933
MonotonicityNot monotonic
2023-07-08T15:55:44.730866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25123
47.8%
44.99666723 1561
 
3.0%
44.99333445 1418
 
2.7%
0.02466640632 420
 
0.8%
0.02483306751 279
 
0.5%
1.49666667 236
 
0.4%
89.99333191 174
 
0.3%
0.04949955697 116
 
0.2%
1.49333334 104
 
0.2%
0.04933289496 104
 
0.2%
Other values (18168) 20597
39.2%
(Missing) 2428
 
4.6%
ValueCountFrequency (%)
0 25123
47.8%
0.0001666666552 2
 
< 0.1%
0.0001666666622 25
 
< 0.1%
0.0001754385885 1
 
< 0.1%
0.0001851851803 1
 
< 0.1%
0.0001960784262 1
 
< 0.1%
0.0003333333104 1
 
< 0.1%
0.0003333333201 3
 
< 0.1%
0.0003333333244 12
 
< 0.1%
0.0003508771836 3
 
< 0.1%
ValueCountFrequency (%)
96.35802674 1
 
< 0.1%
92.77666728 1
 
< 0.1%
92.76333364 1
 
< 0.1%
92.74000041 1
 
< 0.1%
92.49333191 77
0.1%
92.49333191 1
 
< 0.1%
92.46783242 1
 
< 0.1%
92.40033273 1
 
< 0.1%
92.33333333 10
 
< 0.1%
92.33000183 3
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34213
Distinct (%)68.2%
Missing2428
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean64.865282
Minimum13.13
Maximum76.015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:44.833274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.13
5-th percentile46.41688
Q164.25
median67.3875
Q369.267501
95-th percentile71.255
Maximum76.015
Range62.885
Interquartile range (IQR)5.0175009

Descriptive statistics

Standard deviation8.0285709
Coefficient of variation (CV)0.12377301
Kurtosis8.1388915
Mean64.865282
Median Absolute Deviation (MAD)2.2249968
Skewness-2.6492811
Sum3251826.3
Variance64.457951
MonotonicityNot monotonic
2023-07-08T15:55:44.936262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.09999847 19
 
< 0.1%
69.5625 13
 
< 0.1%
68.74000015 11
 
< 0.1%
69 11
 
< 0.1%
70.49249992 11
 
< 0.1%
68.45749969 11
 
< 0.1%
67 10
 
< 0.1%
66.4375 10
 
< 0.1%
67.5 10
 
< 0.1%
68.75 10
 
< 0.1%
Other values (34203) 50016
95.2%
(Missing) 2428
 
4.6%
ValueCountFrequency (%)
13.13000011 1
< 0.1%
13.17000008 1
< 0.1%
13.27250004 1
< 0.1%
13.29500008 1
< 0.1%
13.39500046 1
< 0.1%
13.50749969 1
< 0.1%
13.52499962 1
< 0.1%
13.55000019 1
< 0.1%
13.55500031 1
< 0.1%
13.71250057 1
< 0.1%
ValueCountFrequency (%)
76.01499977 1
< 0.1%
75.92249908 1
< 0.1%
75.58421045 1
< 0.1%
75.52749977 1
< 0.1%
75.32250366 1
< 0.1%
75.28250237 1
< 0.1%
75.25263294 1
< 0.1%
75.20526364 1
< 0.1%
75.18999939 1
< 0.1%
75.15999985 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49811
Distinct (%)94.9%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10.450541
Minimum0
Maximum15.326198
Zeros1142
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:45.057525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.61000001
Q18.2891671
median10.741135
Q313.800764
95-th percentile15.153205
Maximum15.326198
Range15.326198
Interquartile range (IQR)5.511597

Descriptive statistics

Standard deviation3.9939328
Coefficient of variation (CV)0.38217474
Kurtosis0.77486461
Mean10.450541
Median Absolute Deviation (MAD)2.5662583
Skewness-1.0230963
Sum548402.59
Variance15.951499
MonotonicityNot monotonic
2023-07-08T15:55:45.173030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1142
 
2.2%
8.140000343 383
 
0.7%
8.149999619 59
 
0.1%
8.159999847 25
 
< 0.1%
8.25 20
 
< 0.1%
8.170000076 19
 
< 0.1%
8.220000267 19
 
< 0.1%
8.18999958 18
 
< 0.1%
8.180000305 18
 
< 0.1%
8.199999809 16
 
< 0.1%
Other values (49801) 50757
96.6%
(Missing) 84
 
0.2%
ValueCountFrequency (%)
0 1142
2.2%
0.003484000685 1
 
< 0.1%
0.003599500749 1
 
< 0.1%
0.004134000395 1
 
< 0.1%
0.005255500786 1
 
< 0.1%
0.005484000314 1
 
< 0.1%
0.008977502352 1
 
< 0.1%
0.009999999776 4
 
< 0.1%
0.01050000242 9
 
< 0.1%
0.01050000265 1
 
< 0.1%
ValueCountFrequency (%)
15.32619774 1
< 0.1%
15.31790953 1
< 0.1%
15.31597339 1
< 0.1%
15.31168552 1
< 0.1%
15.29916626 1
< 0.1%
15.29889584 1
< 0.1%
15.29477473 1
< 0.1%
15.2942521 1
< 0.1%
15.29328798 1
< 0.1%
15.29100926 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52014
Distinct (%)99.1%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1239.9552
Minimum-151.96815
Maximum1814.1157
Zeros70
Zeros (%)0.1%
Negative3
Negative (%)< 0.1%
Memory size410.8 KiB
2023-07-08T15:55:45.282023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-151.96815
5-th percentile73.845457
Q1985.4642
median1275.0142
Q31635.4086
95-th percentile1795.1777
Maximum1814.1157
Range1966.0838
Interquartile range (IQR)649.94441

Descriptive statistics

Standard deviation472.25582
Coefficient of variation (CV)0.38086524
Kurtosis0.79343825
Mean1239.9552
Median Absolute Deviation (MAD)302.83414
Skewness-1.0310676
Sum65067887
Variance223025.56
MonotonicityNot monotonic
2023-07-08T15:55:45.382539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
0.1%
969.9899902 19
 
< 0.1%
969.9500122 17
 
< 0.1%
970.0200195 17
 
< 0.1%
969.9799805 16
 
< 0.1%
970.0700073 14
 
< 0.1%
970.0499878 14
 
< 0.1%
970.0300293 13
 
< 0.1%
969.960022 13
 
< 0.1%
970 13
 
< 0.1%
Other values (52004) 52270
99.4%
(Missing) 84
 
0.2%
ValueCountFrequency (%)
-151.9681549 1
 
< 0.1%
-90.05035457 1
 
< 0.1%
-39.5872612 1
 
< 0.1%
0 70
0.1%
0.009999999776 2
 
< 0.1%
0.01999999955 1
 
< 0.1%
0.02999999933 1
 
< 0.1%
0.03999999911 1
 
< 0.1%
0.05999999866 2
 
< 0.1%
0.0700000003 3
 
< 0.1%
ValueCountFrequency (%)
1814.115664 1
< 0.1%
1813.054224 1
< 0.1%
1812.533936 1
< 0.1%
1812.272501 1
< 0.1%
1811.922003 1
< 0.1%
1811.406921 1
< 0.1%
1811.135817 1
< 0.1%
1810.878181 1
< 0.1%
1810.659657 1
< 0.1%
1810.516439 1
< 0.1%
Distinct31350
Distinct (%)62.5%
Missing2428
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean11.673565
Minimum-0.205
Maximum36.72
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)< 0.1%
Memory size410.8 KiB
2023-07-08T15:55:45.615032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.205
5-th percentile3.4962222
Q17.2449999
median10.958947
Q315.695
95-th percentile21.570474
Maximum36.72
Range36.925
Interquartile range (IQR)8.4500001

Descriptive statistics

Standard deviation5.7279198
Coefficient of variation (CV)0.49067443
Kurtosis0.033866537
Mean11.673565
Median Absolute Deviation (MAD)4.1110529
Skewness0.51841314
Sum585219.16
Variance32.809065
MonotonicityNot monotonic
2023-07-08T15:55:45.716177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.900000095 55
 
0.1%
7.800000191 53
 
0.1%
7.199999809 49
 
0.1%
7.699999809 48
 
0.1%
7.400000095 47
 
0.1%
6.699999809 47
 
0.1%
8.800000191 45
 
0.1%
8.600000381 45
 
0.1%
8.5 45
 
0.1%
6.300000191 43
 
0.1%
Other values (31340) 49655
94.5%
(Missing) 2428
 
4.6%
ValueCountFrequency (%)
-0.2049999982 1
< 0.1%
-0.1950000077 1
< 0.1%
-0.1325000077 1
< 0.1%
-0.1315789521 1
< 0.1%
-0.08571428806 1
< 0.1%
-0.08250000328 1
< 0.1%
-0.07368421555 1
< 0.1%
-0.0700000003 1
< 0.1%
-0.06315789372 1
< 0.1%
-0.05000000075 1
< 0.1%
ValueCountFrequency (%)
36.72000046 1
< 0.1%
36.71250057 1
< 0.1%
36.54249954 1
< 0.1%
36.52000008 1
< 0.1%
36.49999962 1
< 0.1%
36.48750076 1
< 0.1%
36.46499958 1
< 0.1%
36.34249954 1
< 0.1%
36.27499924 1
< 0.1%
36.20499821 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35902
Distinct (%)71.6%
Missing2428
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean66.799149
Minimum14.04
Maximum79.855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:45.826238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.04
5-th percentile45.615
Q163.990002
median70.8975
Q372.944999
95-th percentile74.597499
Maximum79.855
Range65.815
Interquartile range (IQR)8.9549978

Descriptive statistics

Standard deviation9.5257493
Coefficient of variation (CV)0.14260285
Kurtosis4.0689399
Mean66.799149
Median Absolute Deviation (MAD)2.8212484
Skewness-1.9572447
Sum3348774.9
Variance90.739899
MonotonicityNot monotonic
2023-07-08T15:55:45.924684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.10000038 14
 
< 0.1%
73.35999985 11
 
< 0.1%
72.05499992 10
 
< 0.1%
72.25 10
 
< 0.1%
73.25 10
 
< 0.1%
72.86499977 9
 
< 0.1%
73.525 9
 
< 0.1%
71.36500015 9
 
< 0.1%
71.75 9
 
< 0.1%
72.99999962 9
 
< 0.1%
Other values (35892) 50032
95.2%
(Missing) 2428
 
4.6%
ValueCountFrequency (%)
14.03999996 1
< 0.1%
14.19750023 1
< 0.1%
14.20499992 1
< 0.1%
14.30000019 1
< 0.1%
14.31500053 1
< 0.1%
14.32999992 1
< 0.1%
14.39999962 1
< 0.1%
14.50500011 1
< 0.1%
14.53999996 1
< 0.1%
14.60000038 1
< 0.1%
ValueCountFrequency (%)
79.85499954 1
< 0.1%
78.02499962 1
< 0.1%
78.01999664 1
< 0.1%
77.77000122 1
< 0.1%
77.75 1
< 0.1%
77.74749985 1
< 0.1%
77.71250153 1
< 0.1%
77.70249786 1
< 0.1%
77.69249763 1
< 0.1%
77.68947079 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50122
Distinct (%)> 99.9%
Missing2429
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean52.892865
Minimum2.8795057
Maximum234.37635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:46.026380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.8795057
5-th percentile5.3841964
Q134.418146
median50.886929
Q368.306738
95-th percentile105.27367
Maximum234.37635
Range231.49684
Interquartile range (IQR)33.888592

Descriptive statistics

Standard deviation28.665237
Coefficient of variation (CV)0.54194903
Kurtosis1.0173771
Mean52.892865
Median Absolute Deviation (MAD)16.931707
Skewness0.67166344
Sum2651572.2
Variance821.69579
MonotonicityNot monotonic
2023-07-08T15:55:46.124899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.79227448 2
 
< 0.1%
45.93724775 2
 
< 0.1%
5.068540096 2
 
< 0.1%
46.21323013 2
 
< 0.1%
43.90499496 2
 
< 0.1%
5.278846741 2
 
< 0.1%
67.72183437 2
 
< 0.1%
4.960285592 2
 
< 0.1%
44.99627123 2
 
< 0.1%
77.16502714 1
 
< 0.1%
Other values (50112) 50112
95.3%
(Missing) 2429
 
4.6%
ValueCountFrequency (%)
2.879505652 1
< 0.1%
3.093741518 1
< 0.1%
3.292961645 1
< 0.1%
3.420722878 1
< 0.1%
3.441267961 1
< 0.1%
3.520838058 1
< 0.1%
3.590163696 1
< 0.1%
3.618848316 1
< 0.1%
3.62738061 1
< 0.1%
3.647167945 1
< 0.1%
ValueCountFrequency (%)
234.3763477 1
< 0.1%
224.3239305 1
< 0.1%
220.4856415 1
< 0.1%
214.2883148 1
< 0.1%
212.986799 1
< 0.1%
212.9480137 1
< 0.1%
201.6822002 1
< 0.1%
200.3572396 1
< 0.1%
199.1307358 1
< 0.1%
198.6064165 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct50706
Distinct (%)96.6%
Missing84
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.0726965
Minimum0.13423152
Maximum22.266128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:46.225230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.13423152
5-th percentile2.2
Q14.1399999
median5.8501058
Q37.596334
95-th percentile10.872341
Maximum22.266128
Range22.131896
Interquartile range (IQR)3.4563342

Descriptive statistics

Standard deviation2.708171
Coefficient of variation (CV)0.44595857
Kurtosis1.0571285
Mean6.0726965
Median Absolute Deviation (MAD)1.7264584
Skewness0.74671541
Sum318670.82
Variance7.3341902
MonotonicityNot monotonic
2023-07-08T15:55:46.322904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.980000019 12
 
< 0.1%
2.890000105 11
 
< 0.1%
3.640000105 11
 
< 0.1%
4.159999847 11
 
< 0.1%
2.160000086 11
 
< 0.1%
2.710000038 10
 
< 0.1%
3.779999971 10
 
< 0.1%
3.059999943 10
 
< 0.1%
2.5 10
 
< 0.1%
3.369999886 10
 
< 0.1%
Other values (50696) 52370
99.6%
(Missing) 84
 
0.2%
ValueCountFrequency (%)
0.134231519 1
< 0.1%
0.1956750974 1
< 0.1%
0.2214081138 1
< 0.1%
0.225975408 1
< 0.1%
0.2336064577 1
< 0.1%
0.2683876157 1
< 0.1%
0.2827127084 1
< 0.1%
0.2918813527 1
< 0.1%
0.2934561811 1
< 0.1%
0.2958187461 1
< 0.1%
ValueCountFrequency (%)
22.26612753 1
< 0.1%
21.44596863 1
< 0.1%
20.61954718 1
< 0.1%
20.13348395 1
< 0.1%
20.10885596 1
< 0.1%
20.07139678 1
< 0.1%
19.99107699 1
< 0.1%
19.86361871 1
< 0.1%
19.78643724 1
< 0.1%
19.7748755 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50123
Distinct (%)> 99.9%
Missing2429
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean27.928132
Minimum3.5436597
Maximum219.41041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:46.424773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.5436597
5-th percentile5.5157889
Q117.284946
median25.067366
Q335.419241
95-th percentile57.567003
Maximum219.41041
Range215.86676
Interquartile range (IQR)18.134295

Descriptive statistics

Standard deviation16.442042
Coefficient of variation (CV)0.58872687
Kurtosis5.344257
Mean27.928132
Median Absolute Deviation (MAD)8.8483748
Skewness1.6323027
Sum1400065.2
Variance270.34073
MonotonicityNot monotonic
2023-07-08T15:55:46.524872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.67619324 2
 
< 0.1%
22.68770027 2
 
< 0.1%
4.761767215 2
 
< 0.1%
18.36753244 2
 
< 0.1%
39.45182037 2
 
< 0.1%
39.48072815 2
 
< 0.1%
27.34189162 2
 
< 0.1%
4.803558826 2
 
< 0.1%
41.33267686 1
 
< 0.1%
23.66196251 1
 
< 0.1%
Other values (50113) 50113
95.3%
(Missing) 2429
 
4.6%
ValueCountFrequency (%)
3.543659687 1
< 0.1%
3.565520775 1
< 0.1%
3.572453356 1
< 0.1%
3.585055459 1
< 0.1%
3.603888512 1
< 0.1%
3.614525914 1
< 0.1%
3.627212048 1
< 0.1%
3.638354778 1
< 0.1%
3.672905356 1
< 0.1%
3.701365316 1
< 0.1%
ValueCountFrequency (%)
219.4104148 1
< 0.1%
206.0144428 1
< 0.1%
162.2075304 1
< 0.1%
159.4526632 1
< 0.1%
155.2344716 1
< 0.1%
151.1464987 1
< 0.1%
146.0311305 1
< 0.1%
144.5266539 1
< 0.1%
144.5139687 1
< 0.1%
143.9198761 1
< 0.1%
Distinct25
Distinct (%)< 0.1%
Missing2429
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean675.20147
Minimum660
Maximum687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T15:55:46.612326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile664
Q1668
median674
Q3682
95-th percentile685
Maximum687
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.2923165
Coefficient of variation (CV)0.010800208
Kurtosis-1.3791942
Mean675.20147
Median Absolute Deviation (MAD)7
Skewness-0.029209923
Sum33848525
Variance53.17788
MonotonicityIncreasing
2023-07-08T15:55:46.699459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
682 11149
21.2%
685 4901
9.3%
673 4595
8.7%
667 4463
8.5%
679 3163
 
6.0%
664 2896
 
5.5%
668 2824
 
5.4%
666 2626
 
5.0%
671 2531
 
4.8%
687 1964
 
3.7%
Other values (15) 9019
17.2%
(Missing) 2429
 
4.6%
ValueCountFrequency (%)
660 415
 
0.8%
663 37
 
0.1%
664 2896
5.5%
665 134
 
0.3%
666 2626
5.0%
667 4463
8.5%
668 2824
5.4%
669 1772
 
3.4%
670 1492
 
2.8%
671 2531
4.8%
ValueCountFrequency (%)
687 1964
 
3.7%
685 4901
9.3%
684 21
 
< 0.1%
683 67
 
0.1%
682 11149
21.2%
681 6
 
< 0.1%
680 489
 
0.9%
679 3163
 
6.0%
678 947
 
1.8%
677 116
 
0.2%

Interactions

2023-07-08T15:55:41.852519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:27.842690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.015548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.154662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.300910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.483276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.620431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.781917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.042786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.219894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.385324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.501417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.735477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.932698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:27.931715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.099842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.236869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.377335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.566420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.703503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.862449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.124820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.301054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.464034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.585535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.814806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.029516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.016545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.190348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.328846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.463404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.660095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.795362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.955459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.216619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.395371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.554836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.675478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.902422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.119050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.104234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.279036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.417116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.549239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.752946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.887527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.046921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.320116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.490721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.640588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.880625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.996206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.202319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.185323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.360456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.503492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.628336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.840401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.973974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.135097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.408129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.580985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.722879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.958177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.080315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.291332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.269344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.453036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.595953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.712004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.926818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.067751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.229392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.502153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.672552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.808526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.048540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.166921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.386816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.355936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.546814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.691101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.800271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.021436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.160001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.324988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.596047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.767625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.899665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.141901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.257974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.483796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.447593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.639282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.786589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.888759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.115038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.253917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.416042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.691114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.862117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.995877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.234528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.347824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.576983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.601521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.729207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.880970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.975980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.205258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.349623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.508887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.781042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.953789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.084083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.326678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.438666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.667034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.689061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.822753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.968676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.060103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.291150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.443679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.597367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.873648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.040870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.175026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.414203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.529913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.750132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.768846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.903065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.053706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.139027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.373883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.526741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.680677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:36.958700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.124419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.252008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.494364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.610833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.831866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.847310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:29.989625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.134800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.320553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.452431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.612695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.763327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.046844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.209040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.334609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.570028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.689432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:42.913069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:28.930871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:30.068790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:31.214600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:32.397877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:33.536106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:34.694540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:35.957053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:37.131910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:38.295326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:39.414708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:40.650416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T15:55:41.767618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T15:55:46.781962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.1110.107-0.2180.6160.9940.994-0.1760.9040.5360.9770.776-0.007
Wind direction (°)0.1111.0000.8880.0150.0470.1100.110-0.0380.0970.0980.1110.134-0.103
Nacelle position (°)0.1070.8881.0000.0140.0460.1060.105-0.0330.0930.0890.1010.123-0.109
blade_angle-0.2180.0150.0141.000-0.519-0.224-0.2250.115-0.2980.004-0.191-0.016-0.064
Rear bearing temperature (°C)0.6160.0470.046-0.5191.0000.6160.6130.1010.7790.2800.5960.3870.091
Rotor speed (RPM)0.9940.1100.106-0.2240.6161.0000.999-0.1690.9040.5350.9710.774-0.005
Generator RPM (RPM)0.9940.1100.105-0.2250.6130.9991.000-0.1790.9040.5360.9700.774-0.007
Nacelle ambient temperature (°C)-0.176-0.038-0.0330.1150.101-0.169-0.1791.000-0.130-0.163-0.152-0.1630.164
Front bearing temperature (°C)0.9040.0970.093-0.2980.7790.9040.904-0.1301.0000.4650.8810.674-0.004
Tower Acceleration X (mm/ss)0.5360.0980.0890.0040.2800.5350.536-0.1630.4651.0000.4850.837-0.050
Wind speed (m/s)0.9770.1110.101-0.1910.5960.9710.970-0.1520.8810.4851.0000.745-0.001
Tower Acceleration y (mm/ss)0.7760.1340.123-0.0160.3870.7740.774-0.1630.6740.8370.7451.000-0.049
Metal particle count counter-0.007-0.103-0.109-0.0640.091-0.005-0.0070.164-0.004-0.050-0.001-0.0491.000

Missing values

2023-07-08T15:55:43.036458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T15:55:43.241342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T15:55:43.587997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00191.814896284.644043277.1467900.07465966.8550038.8804231055.6340339.01500068.13250071.0835954.45406023.661963660.0
12019-01-01 00:10:00189.524963288.776154277.1467900.06499966.0625008.8596001053.6279309.01000066.58000264.6999134.33143625.722168660.0
22019-01-01 00:20:00381.144653292.093292277.1467900.00000068.61499810.6013791258.7933359.00000069.66000463.8814165.44721023.538246660.0
32019-01-01 00:30:00422.717590294.645264277.1467900.00000070.90249610.9165131297.3439949.00000072.73999852.3183785.47290018.273508660.0
42019-01-01 00:40:00307.518646299.022400296.1253660.00000070.0325019.9973381187.4628918.98000071.84000460.1356475.60127823.249350660.0
52019-01-01 00:50:00100.532738307.804749310.0737300.44633167.3274998.298341985.8508918.99000068.07500581.8323823.77354028.982244660.0
62019-01-01 01:00:0051.613712311.235474310.0737300.94133665.4650048.184303973.3662118.98250064.63999983.9087303.01836230.293451660.0
72019-01-01 01:10:00152.640259302.836853310.0737300.09933165.9875038.5071531011.3021858.90250065.15999672.3797384.55944032.465843660.0
82019-01-01 01:20:00188.583817299.069946310.0737300.00000066.9049998.7742261043.2574468.74000166.69500051.0578314.85721919.690979660.0
92019-01-01 01:30:00154.423843287.232666307.8849490.07416666.4950038.4998731010.7999888.64500066.50000056.1486634.41313028.461374660.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00139.18988799.395604115.8048860.14549465.0525018.393873998.7773557.125064.77000070.5058084.62919721.036877687.0
525512019-12-31 22:30:00191.97822099.349597115.8048860.00000065.3775008.7781641043.7732477.092565.25000153.8996204.68551622.165400687.0
525522019-12-31 22:40:00131.73105190.048425109.4268550.13766564.7775008.298261986.5212946.985064.47750078.1058074.20055228.042950687.0
525532019-12-31 22:50:00140.83950391.18616474.0977170.00000064.7249988.230955979.5445006.785064.37000252.8714684.17408225.838930687.0
525542019-12-31 23:00:00129.15143598.01211074.0977170.07416664.6224998.247364980.6057916.705064.13250164.2817403.98338928.019881687.0
525552019-12-31 23:10:00121.942842100.69045474.2622730.12349964.3175008.206786976.1668286.672563.62250054.7177193.69025026.458088687.0
525562019-12-31 23:20:00171.36356091.33763596.9270880.00000064.9025008.6041541024.4437926.610064.52750059.9111924.34884823.043759687.0
525572019-12-31 23:30:00232.95789292.15374197.1464160.00000065.8800009.2378131098.7448336.400065.97500061.1367695.03885321.719752687.0
525582019-12-31 23:40:00282.13574698.66020997.1464160.00000067.0900009.7400811157.1135416.435067.93999949.3634795.47783821.770018687.0
525592019-12-31 23:50:00193.669499110.71071197.1464160.00000066.1500008.8056061045.3684046.627567.11000193.0609484.47349531.562533687.0